optuna.visualization.matplotlib.plot_rank(study, params=None, *, target=None, target_name='Objective Value')[source]

Plot parameter relations as scatter plots with colors indicating ranks of target value.

Note that trials missing the specified parameters will not be plotted.

See also

Please refer to optuna.visualization.plot_rank() for an example.


Output figures of this Matplotlib-based plot_rank() function would be different from those of the Plotly-based plot_rank().


The following code snippet shows how to plot the parameter relationship as a rank plot.

import optuna

def objective(trial):
    x = trial.suggest_float("x", -100, 100)
    y = trial.suggest_categorical("y", [-1, 0, 1])

    c0 = 400 - (x + y)**2
    trial.set_user_attr("constraint", [c0])

    return x ** 2 + y

def constraints(trial):
    return trial.user_attrs["constraint"]

sampler = optuna.samplers.TPESampler(seed=10, constraints_func=constraints)
study = optuna.create_study(sampler=sampler)
study.optimize(objective, n_trials=30)

optuna.visualization.matplotlib.plot_rank(study, params=["x", "y"])
  • study (Study) – A Study object whose trials are plotted for their target values.

  • params (list[str] | None) – Parameter list to visualize. The default is all parameters.

  • target (Callable[[FrozenTrial], float] | None) –

    A function to specify the value to display. If it is None and study is being used for single-objective optimization, the objective values are plotted.


    Specify this argument if study is being used for multi-objective optimization.

  • target_name (str) – Target’s name to display on the color bar.


A matplotlib.axes.Axes object.

Return type:



Added in v3.2.0 as an experimental feature. The interface may change in newer versions without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.2.0.